How To Be A Successful Data Scientist When You Know Nothing About SQL

Overview:

it is undeniable that the importance of data science in a business setting has increased recently, and consequently, so has the importance of SQL skills. SQL is a crucial programming language that allows users to ask for information from the database. A developer's knowledge of data management would allow them to reduce the costs of analyzing data, increasing their efficiency. 

A data scientist who does not know SQL may be able to make use of a tool that allows them to write SQL code in order to perform certain operations on the database, but if they want more than this, then they will need to learn how to write their own SQL code.

In this article, I will outline some of the features you need to know about SQL and what data should look like when presented using it. So grab a glass of water, and get ready for an enlightening journey through the world of SQL.

Introduction to SQL for Data Science:

SQL is a language for querying and manipulating databases. Data scientists use SQL to manipulate data to create graphs, charts, and other visualizations. They also use SQL to build predictive models that can help them in making critical business decisions.

In other words, it is a set of rules and guidelines to produce valid, structured data. A database developer with a good understanding of SQL can write efficient SQL queries, extract relevant information from each record in the database, and deploy changes efficiently without causing any errors in the system.

Here are some SQL skills that will be useful for data scientists:

• Converting between different types of data (e.g., from one format to another)

• Describing the structure of a table or database (e.g., how many columns does it have?)

• Getting information about specific rows (e.g., how many rows have specific values in them?)

For example, if you want to find out how many people have purchased a certain product, you would write:

SELECT COUNT(*) FROM products WHERE product_id = '123' AND category = 'food' AND quantity > 10

The first query (SELECT COUNT) tells the database to return all the rows in your table. The second query (FROM) tells the database which table you want it to look at and how many rows there are. The third and fourth queries tell the database which field of information from each row should be returned.

If we were looking at all of our customers who purchased a certain product but only wanted their names and addresses, we could write:

SELECT customer_name FROM customers HAVING product_id = '123' AND category = 'food' AND quantity > 10

How is SQL important for data science?

For data science, SQL knowledge is important for multiple reasons: 

• To access data, it must be extracted from the database. That is when SQL enters the picture. Since data Science relies heavily on Relational Database Management (RDBS), a Data Scientist can manage, define, alter, create, and query the database using SQL commands. 

• SQL is also an important tool for data cleaning and preparation. Thus, knowledge of SQL can help you to clean up your data and store it in a structured form. 

• Also, you will need to run complex queries on your data to derive the intended results. Knowledge of SQL is enough to produce good results if most of what you want is one standard report or table. 

• Moreover, many big data tools give you free-to-use powerful ways to explore datasets or even work with full relational databases directly from them (e.g., Spark/Hadoop).

• Data science tools like Python, R, or Stata have SQL connectors that need SQL skills to perform tasks related to data analysis and preparation of data for modeling.

Therefore, SQL has been used by many data scientists to analyze big datasets and extract useful insights. If you want to become a pro data scientist and make your mark in the field, you will have to learn SQL. For more in-depth knowledge, take the data science certification course and become an expert in SQL.

How to get started with SQL in data science? 

1. Basics of Relational Databases:

Understanding the fundamentals of a relational database is a necessary prerequisite for diving into the world of SQL. A relational database is a structured collection of data presented in tabular format. In relational database management systems, terms like:

• Tables - known as relations

• Records - no. of rows in the database (Also known as a tuple) 

• Primary Key - a value that identifies the information

• Foreign Key - links the primary table to the secondary table

• Attributes - data categories available in columns

Once you've mastered the basics of relational databases, learn the basics of SQL and SQL commands. SQL is based on relational algebra, which specifies a set of logical principles for describing data.

2. Data manipulation language(DML):

Using data manipulation language, it is possible to add, remove, and modify the data in a database. Several DML commands include:

• INSERT - For inserting/adding records into the table

• UPDATE - for modifying columns/rows in the database

• DELETE - For deleting rows/columns

3. Data Definition Language (DDL):

The Data Definition Language (DDL) is yet another essential SQL command (DDL). It is a tool for describing and manipulating database structures.

For instance, the structure of a table can be modified by the creation, deletion, or modification of a table.

Some of the common DDL commands are:

• CREATE - For creating a new table

• ALTER - For altering the structure of the database

• DROP - For deleting an entire record in a database

4. SQL joins:

Joins are the most crucial topics in SQL. Many interview questions revolve around joining tables and views in SQL. In general, SQL join statements allow you to integrate columns from many tables into one. When just one table is involved in the join, it is self-joining.

Some of the important joins are:

• INNER JOINS

• RIGHT JOINS

• LEFT JOINS

• FULL JOINS

5. Learn to interface SQL with Python and R

The last thing you should consider is that R and Python are the two popular programming languages that utilize SQL for retrieving data. It is the initial phase in collecting data for subsequent processing and analysis. 

Many data scientists and analytics practitioners have found success with SQL-backend tools for analysis. Yet, Python remains the go-to language for many tasks like data manipulation and machine learning (and increasingly, R). However, it's certainly possible to do most analytics tasks in SQL, especially since it's been getting more and more powerful over time. 

Conclusion:

In summary, SQL is an incredibly important skill to learn in data science. No matter what your data science job description says, the chances are that some amount of SQL skill is required. SQL will give you a grounding in querying, which will help you with any data you encounter. And it's only getting more important as the world of data science becomes more advanced. I recommend learning it if you haven't already! If you're wondering where to learn, check out the data science course in Chennai and become a pro in SQL for data science.